3. Visual Analytics (Part 1: Visual Encoding) Jacobs University Visualization and Computer Graphics Lab
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1 3. Visual Analytics (Part 1: Visual Encoding)
2 3.1 Introduction
3 Motivation Big Data cannot be analyzed anymore without the help of computers. Computers are good in quickly processing large amounts of data. Humans are good in making decisions based on their expertise. Humans can visually process information in parallel. Visuals are intuitive. [Tidwell] Data Analytics 81
4 Motivation Visual analytics combines visual data representations with interactive and automatic data analysis components. Data Analytics 82
5 Goals Obtain insight into the given data: 1. Answering specific questions: quantitative: What are the data values and their distribution? qualitative: Is this feature occurring in the data? 2. Discovering the unknown: What is in the data set? Data Analytics 83
6 Definition of Visualization Creating images that convey salient information about underlying data and processes NSF report: a method for seeing the unseen new scientific insight through visual methods Data Analytics 84
7 Definition Visualization is the process of extracting salient features from sets of data and displaying the features in an intuitive and expressive way. Data Analytics 85
8 Definition Visualization is the process of extracting salient features from sets of data and displaying the features in an intuitive and expressive way. Find and develop step-wise methods for feature extracting feature display Implement the steps of the methods on a computer Algorithm Data Analytics 86
9 Definition Visualization is the process of extracting salient features from sets of data and displaying the features in an intuitive and expressive way. Application-specific Anything that can be relevant to the user Characteristics and properties of data Data Analytics 87
10 Definition Visualization is the process of extracting salient features from sets of data and displaying the features in an intuitive and expressive way. Collected data using measurements simulations Spatial data -> Scientific Visualization Non-spatial data -> Information Visualization Data Analytics 88
11 Definition Visualization is the process of extracting salient features from sets of data and displaying the features in an intuitive and expressive way. Render on a computer screen Allow for interaction Animations Computer graphics Data Analytics 89
12 Definition Visualization is the process of extracting salient features from sets of data and displaying the features in an intuitive and expressive way. The user must be able to understand the produced images without major training Misleading visualizations are to be avoided The extracted feature needs to be visually documented Data Analytics 90
13 Visualization Process Data Analytics 91
14 3.2 Historical examples
15 Snow s map of cholera in London 1663 Data Analytics 93
16 Minard s map of Napoleon s march on Moscow drawn in 1869 Data Analytics 94
17 Playfair s plot of trade between England and Denmark/Norway from 1786 Data Analytics 95
18 Nightingale s coxcomb chart showing monthly deaths in army from 1858 Data Analytics 96
19 3.3 Principles and Best Practices
20 Well-known examples pie charts bar charts scatter plots Data Analytics 98
21 Good and bad visualizations Data Analytics 99
22 Good and bad visualizations Data Analytics 100
23 Good and bad visualizations Data Analytics 101
24 Good and bad visualizations Data Analytics 102
25 Good and bad visualizations Data Analytics 103
26 Good and bad visualizations Data Analytics 104
27 Good and bad visualizations Data Analytics 105
28 Good and bad visualizations Data Analytics 106
29 Good and bad visualizations Data Analytics 107
30 Good and bad visualizations Data Analytics 108
31 Good and bad visualizations Data Analytics 109
32 Good and bad visualizations Data Analytics 110
33 Good and bad visualizations Data Analytics 111
34 Good and bad visualizations Data Analytics 112
35 Good and bad visualizations Data Analytics 113
36 Good and bad visualizations Data Analytics 114
37 Ascombe s quartett Same mean, variance, correlation, and regression lines Data Analytics 115
38 3.4 Visual encoding
39 Visual encoding We have seen that attributes may have different characteristics (numerical, ordered, categorical). Special attributes are time (temporal dimension) and geographical location (spatial dimensions) in case of spatiotemporal data. Entities are typically visually encoded by some kind of glyph. Glyphs vary with respect to position, size, shape, orientation, color, and texture. Visual encoding is the mapping of attributes of the entity to specifics of the glyph. Data Analytics 117
40 Visual encoding The exact visual encoding of a data set depends on the number and characteristics of the available attributes and on the analysis tasks of the given application. Interaction mechanisms allow for switching between different attributes to allow for different views on the attributes and to fulfill different tasks. Still, there are guidelines for mapping attributes to visual cues. Data Analytics 118
41 Visual cues color texture shape Data Analytics 119
42 Guidelines location size color orientation texture shape geospatial time numerical (continuous) numerical (discrete) ordered categorical Data Analytics 120
43 Perception One needs to consider that certain visual cues are more perceptionally relevant than others. Color is a pre-attentive feature that can be seen immediately. Location and size are also easy to perceive. Shape is more difficult to distinguish. Orientation and texture do not allow for detecting subtle changes easily. Data Analytics 121
44 Jacque Bertin s semiology of graphics (1967) Data Analytics 122
45 3.5 Assignment Data Analytics 123
46 Assignment 2 1. Download the Automobile Data Set provided here: 2. Parse the data to automatically extract the following attributes: make (3), fuel-type (4), body-style (7), curb-weight (14), num-of-cylinders (16), engine-size (17), horsepower (22), city-mpg (24), highway-mpg (25), and price (26). 3. Map each data attribute to a suitable visual encoding. 4. Implement the visual encodings of the individual attributes. 5. Develop strategies for visually encoding combinations of attributes (and implement where feasible). 6. What questions can you answer with your visual representations? 7. Discuss your findings. Data Analytics 124
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